Your learning path: 93 lessons.
Module 13 / Lesson 72 · Read, apply, check
Reconcile one product’s identity, price and availability across its visible page and machine-readable representations.
Identify the exact product and variant before checking fields. A model, a color variant and a bundle may not share the same identifier or offer. Keep a stable internal record so the website, structured data and any merchant feed describe the same item.
Review visible title, image, price, currency, stock state and landing URL against the structured data and feed where used. A valid JSON structure can still contain the wrong price. The business’s actual offer is the reference point, not whichever system updated most recently.
Use current feature documentation and a structured-data test to investigate supported markup. Read errors and warnings in context. Passing validation is not proof of complete accuracy or a promise of rich-result display, and merchant platform requirements may add separate checks.
Trace mismatches to synchronization, caching, manual overrides or variant selection. Fix the source of recurring errors, then test a representative set of items. Assign responsibility for monitoring changes in price and stock so the correction survives the next catalog update.
Blue lamp · £45 · in stock.
White lamp · £40: wrong variant and price.
Blue ID, but landing URL selects white.
Identity, offer and selected destination agree across all surfaces.
Hypothetical example: a lamp’s visible page shows the blue variant at £45 in stock, while its structured data still describes a £40 white variant. A feed export contains the blue identifier but an old landing URL that opens the white option. The analyst does not simply edit one price field to make a validator green. She follows the variant mapping, corrects the source record and checks that the blue URL opens the blue image, current price and stock state. The team then validates the markup and inspects the feed record again. The result is a consistent product representation, with no assumption that validation guarantees a particular shopping placement.
Another shop has matching price and availability across its visible page and markup, yet receives no enhanced search display. Changing accurate values to imitate a competitor would introduce errors without diagnosing the absence of display. The team should review eligibility and platform requirements, then continue monitoring. Technical validity and factual consistency are necessary quality checks, but selection for a particular result presentation remains outside the merchant’s direct control.
Misconception: a structured-data test verifies the commercial truth of every field. It can detect supported structure and some issues, but the marketer must still compare values with the actual sellable item and current offer.
Product requirements vary by platform, market and item type. Check the current documentation for the intended feature before a production change. Use a test export when you lack authority to alter live catalog or merchant data.
Do this today · 35 minutes
Reconcile one product’s identity, price and availability across its visible page and machine-readable representations.
One product page, its markup and a feed row if available; otherwise use the lamp record.
Blue lamp: actual offer £45/in stock. Page: blue/£45. Markup: white/£40. Feed: blue identifier but a URL opening white.
Before: Blue item advertised with white-variant markup and landing behavior.
After: Blue identifier, image, £45 price, stock state and selected landing variant agree.
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Repeat the comparison after the next price or inventory update and confirm that the source correction prevents the mismatch from returning.
Before you move on
A blue lamp is available for £45. Its visible page is correct, its markup describes the white £40 variant, and its feed URL opens the white option. Which workflow most completely addresses the discrepancy before seeking enhanced product visibility?
Next lesson →Google: Product variant markup
Google: Structured data introduction
Lesson edition 1 · References reviewed 15 October 2026 · Practical exercise reviewed 15 October 2026. Examples are illustrative teaching scenarios.
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